Data driven methods for deriving amplitude-based motion characterizations in PET imaging

ABSTRACT

Various systems and methods for generating images are provided. In some embodiments, the techniques can include acquiring a medical image and an associated motion characterization. The motion characterization can then be used to generate a plurality of gated image data sets, sorted by phase in the motion cycle. A new amplitude-based motion characterization curve is derived from the association of phases with amplitude-based characteristics in the phase gated images. This newly derived amplitude-based motion characterization curve can then be used to re-sort data according to amplitude-based gating techniques known in the field or with data driven optimization techniques.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/210,769, filed on Aug. 27, 2015, entitled “Data Driven Methods ForDeriving Amplitude Based Motion Characterizations In PET Imaging,” whichis hereby incorporated by reference for all purposes in its entirety.

TECHNICAL FIELD

Various embodiments of the present invention generally relate topositron emission tomography. More specifically, some embodiments of thepresent invention relate systems and methods for data driven methods forderiving amplitude-based motion characterizations in positron emissiontomography imaging.

BACKGROUND

Gating is a strategy for correcting cardiac and respiratory patientmotion which occurs during medical imaging. Gating works by subjugatingraw data into separate bins, which correlate with separate segments ofthe motion cycle. By applying such traditional gating techniques,systems are able to achieved improved resolution, but at the cost ofimage statistics.

Several methods have been proposed for sorting gated data in an“optimal” way. Because patient motion characterization traditionallycomes in an amplitude vs time format, usually from a hardware baseddevice, most strategies to date have either sorted raw data byamplitude, or time. More specifically, data is subjugated into gateddata by separating it by its associated amplitude, or the phase (timebetween cycles) it was acquired at.

Both amplitude and phase based gating strategies have advantages anddisadvantages. Amplitude-based strategies can be used to optimallysegregate data, but they also suffer from drifting of hardware (changingbaseline), are subject to instrument noise, and are less ideal to usewith data driven motion characterization strategies. Phase-based gatingcan be more robust, and has advantages associated with equal segregationof statistics.

SUMMARY

Various systems and techniques for image generation are provided. Inaccordance with some embodiments, medical images and an associatedmotion characterization can be acquired (e.g., from a database or froman imaging system). The motion characterization can then used togenerate a plurality of gated image data sets, sorted by phase in themotion cycle. A new amplitude-based motion characterization curve can bederived from the association of phases with amplitude-basedcharacteristics in the phase gated images. This newly derivedamplitude-based motion characterization curve can then be used tore-sort data according to amplitude-based gating techniques known in thefield or with data driven optimization techniques presented here.

Embodiments of the present invention also include computer-readablestorage media containing sets of instructions to cause one or moreprocessors to perform the methods, variations of the methods, and otheroperations described herein.

Some embodiments can include acquiring a set of phase gated medicalimages of a patient collected via a medical imaging procedure (e.g.,positron emission tomography, magnetic resonance imaging, ultrasound,single-photon emission computed tomography, or planar gamma cameraimaging). A principal component analysis can be applied across phases ofthe set of phase gated medical images to generate an phase-motionamplitude curve describing an amplitude of motion of a patient duringthe medical imaging procedure as a function of phase of a periodicmotion cycle. In some embodiments, the phase-motion amplitude curve isbased on the first principal component. An acceptance window can beidentified based on variations in the non-first principal componentfluctuations. Then, an optimal segregation of image data can bedetermined based on the phase-motion amplitude curve. Single medicalimages can then be generated based on the optimal segregation of theimage data.

In some embodiments, determining optimal segregation of the image datacan include analyzing each point on the phase-motion amplitude curve toassess how many other curve data points are within the acceptancewindow. In addition, determining the optimal segregation can includeclassifying each image based on placement on the phase-motion amplitudecurve and grouping medical images from the set of medical images basedon the classification. A position of the patient within each medicalimage in the set of medical images can be characterized in someembodiments by using one or more of a correlative measure or a signaldisplacement measure.

In some embodiments, a set of phase gated medical images of a patientcollected via a medical imaging procedure can be acquired. By applying aprincipal component analysis of the set of medical images, an indicationof amplitude fluctuations across the phases representing motion of thepatient during the medical imaging procedure can be generated. Theprincipal component analysis of the set of images includes identifying aset of pixels across each medical image in the set of medical imagesrepresenting a common point of interest. Motion of the patient can becharacterized based on the indication of phase and amplitudefluctuations.

In some embodiments, the indication of phase and amplitude fluctuationscan be analyzed to determine optimal bin sizes for sorting the set ofmedical images to maximize image resolution. Then, a set of gatedmedical images can be generated based on sorting of image data. In someembodiments, the set of gated medical images may only be generated whenthe image resolution is improved by at least a set threshold of detectedmotion.

Some embodiments provide for a system comprising a memory, one or moreprocessors, an image acquisition module, a motion characterizationmodule, an optimization module, and an image processing module. Someembodiment may include other components or machines, such as but notlimited to imaging systems (e.g., a positron emission tomographymachine, a magnetic resonance imaging machine, an ultrasound machine, asingle-photon emission computed tomography machine, or a planar gammacamera imaging machine.) The image acquisition module can be configuredto acquire a set of medical images of a patient collected via a medicalimaging procedure. The motion characterization module can be configuredto characterize gating motion of the patient based on the set of medicalimages. The optimization module can be configured to determine optimalbin sizes that maximize image resolution. The image processing module,under the control of the processor, can be configured to generate a setof gated medical images by sorting the set of medical images into binsbased on the optimal bin sizes and combining medical images within eachof the bins to create the set of gated medical images.

In some embodiments, the motion characterization module can apply aprincipal component analysis of the set of medical images to generate anindication of phase and amplitude fluctuations representing gating ofthe patient during the medical imaging procedure. The motioncharacterization module may be further configured to identify a centerof mass measurement, calculate a gradient displacement, or a minimize acost function or characterize a position of the patient within eachmedical image in the set of medical images by using one or more of acorrelative measure, a signal displacement measure, or a principalcomponent analysis. The correlative measure can include a Pearsoncorrelation or a mutual information correlation and the signaldisplacement measure is based on a center of mass.

While multiple embodiments are disclosed, still other embodiments of thepresent invention will become apparent to those skilled in the art fromthe following detailed description, which shows and describesillustrative embodiments of the invention. As will be realized, theinvention is capable of modifications in various aspects, all withoutdeparting from the scope of the present invention. Accordingly, thedrawings and detailed description are to be regarded as illustrative innature and not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present technology will be described and explainedthrough the use of the accompanying drawings in which:

FIG. 1 illustrates an example of an imaging system in which someembodiments of the present technology may be utilized;

FIG. 2 illustrates a set of components within an image processing deviceaccording to one or more embodiments of the present technology;

FIG. 3 is a flowchart illustrating a set of operations for generatinggated images according to one or more embodiments of the presenttechnology;

FIG. 4 is a flowchart illustrating a set of operations for analyzingimages according to one or more embodiments of the present technology;

FIG. 5 is a flowchart illustrating a set of operations for generating anamplitude motion characterization curve and amplitude-based imagesthrough characterization of amplitude properties on phase gated datasets in accordance with some embodiments of the present technology;

FIG. 6 is a flowchart illustrating a set of operations for generating anamplitude motion characterization curve and amplitude-based imagesthrough characterization of amplitude properties on phase gated imagesin accordance with one or more embodiments of the present technology;

FIG. 7 is a flowchart illustrating a set of operations for segregationof data using structured data sets and random data sets in accordancewith some embodiments of the present technology;

FIG. 8 illustrates a data driven motion curve from a patient withsignificant motion in accordance with various embodiments of the presenttechnology;

FIG. 9 illustrates a data drive motion curve and optimal phaseacceptance window according to one or more embodiments of the presenttechnology;

FIG. 10 illustrates images derived from signal binned, optimal binned,and fully binned data in accordance with various embodiments of thepresent technology;

FIG. 11 illustrates a data driven motion curve from a patient withnon-significant motion in accordance with various embodiments of thepresent technology;

FIG. 12 illustrates a data drive motion curve and optimal phaseacceptance window according to one or more embodiments of the presenttechnology;

FIG. 13 illustrates images derived from signal binned, optimal binned,and fully binned data in accordance with various embodiments of thepresent technology;

FIGS. 14A-14C illustrate various plots of data sorting determinationsbased upon phase amplitude characterization in accordance with variousembodiments of the present technology; and

FIG. 15 is a computer system that may be used according to variousembodiments of the present technology.

The drawings have not necessarily been drawn to scale. Similarly, somecomponents and/or operations may be separated into different blocks orcombined into a single block for the purposes of discussion of some ofthe embodiments of the present technology. Moreover, while thetechnology is amenable to various modifications and alternative forms,specific embodiments have been shown by way of example in the drawingsand are described in detail below. The intention, however, is not tolimit the technology to the particular embodiments described. On thecontrary, the technology is intended to cover all modifications,equivalents, and alternatives falling within the scope of the technologyas defined by the appended claims.

DETAILED DESCRIPTION

Respiratory gating is a strategy for correcting cardiac and respiratorymotion patient motion in PET imaging. To implement gating, there firstmust be a characterization of the patient's motion, which can beprovided by hardware or software devices. However, these motioncharacterizations do not always correlate with internal motion (i.e. therelevant signal).

Motion characterization can be used to gate data, using eitherphase-based subjugation of data (e.g. in the time domain), or byamplitude-based methods (e.g. in the motion amplitude domain). Oftenphase-based gating is easier and more robust, but it has been shown thatamplitude-based gated images may be more useful clinically.

Various embodiments of the present invention use the information in thephase-based subjugated data to characterize an amplitude motion model.In some embodiments, an initial motion characterization can be used tosort image data into phase gated data sets, containing images that spanthe phases of a periodic cycle (e.g., a cardiac cycle, a respiratorycycle, etc.). A secondary, amplitude representative motioncharacterization can then be derived from the phase gated image dataset, through use of fluctuation data drive correlative measures (e.g.,principal component analysis, Pearson correlation, mutual information,etc.) or with signal displacement measures (e.g., center of mass)measured at each phase. The resultant data driven amplitude motioncharacterization then describes the relationship between phases of theperiodic cycle and the extracted data driven amplitude values. Thisdefined relationship can then be used to convert an initial motioncharacterization defined in time-phase dimensions to a new motioncharacterization defined in time-data driven amplitude dimensions.

The motion characterization defined in time-data driven amplitudedimensions can then be used for amplitude-based gating, or optimalperiod of data determination and reconstruction, as is described inliterature. In another embodiment, the processes can be used to bothgenerate an amplitude-based motion characterization, as well as a timedependent strength of correlation measure, which can be used todetermine the optimal segments of data to be utilized.

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of embodiments of the present invention. It will beapparent, however, to one skilled in the art that embodiments of thepresent invention may be practiced without some of these specificdetails. For example, while some embodiments use gated PET data, otherembodiments extend to other imaging modalities, such as, but not limitedto, SPECT, MRI, ultrasound, etc.

Moreover, the techniques introduced here can be embodied asspecial-purpose hardware (e.g., circuitry), as programmable circuitryappropriately programmed with software and/or firmware, or as acombination of special-purpose and programmable circuitry. Hence,embodiments may include a machine-readable medium having stored thereoninstructions that may be used to program a computer (or other electronicdevices) to perform a process. The machine-readable medium may include,but is not limited to, floppy diskettes, optical discs, compact discread-only memories (CD-ROMs), magneto-optical discs, ROMs, random accessmemories (RAMs), erasable programmable read-only memories (EPROMs),electrically erasable programmable read-only memories (EEPROMs),application-specific integrated circuits (ASICs), magnetic or opticalcards, flash memory, or other type of media/machine-readable mediumsuitable for storing electronic instructions.

Terminology

Brief definitions of terms, abbreviations, and phrases used throughoutthis application are given below.

The phrases “in some embodiments,” “according to some embodiments,” “inthe embodiments shown,” “in other embodiments,” and the like generallymean the particular feature, structure, or characteristic following thephrase is included in at least one implementation of the presentinvention, and may be included in more than one implementation. Inaddition, such phrases do not necessarily refer to the same embodimentsor different embodiments.

If the specification states a component or feature “may”, “can”,“could”, or “might” be included or have a characteristic, thatparticular component or feature is not required to be included or havethe characteristic.

The term “module” refers broadly to a software, hardware, or firmware(or any combination thereof) component. Modules are typically functionalcomponents that can generate useful data or other output using specifiedinput(s). A module may or may not be self-contained. An applicationprogram (also called an “application”) may include one or more modules,or a module can include one or more application programs.

General Description

FIG. 1 illustrates an example of imaging system 100 in which someembodiments of the present technology may be utilized. Imaging system100 can be any system capable of collecting medical images, such as butnot limited to a positron emission tomography machine, a magneticresonance imaging machine, an ultrasound machine, a single-photonemission computed tomography machine, or other machine. A patient can bepositioned on the moveable platform or bed 110 that can move to placethe patient within the detection mechanism 120. Imaging system 100 canthen scan portions of the patient (i.e., regions of interest) to createsets of medical images. In some embodiments, imaging system 100 mayinclude one or more motion sensors to detect motion of the patient(e.g., cardiac or respiratory motion). The set of medical images, motioninformation, and/or other data may be stored within a database (notshown) for retrieval and processing.

FIG. 2 illustrates a set of components within an image processing deviceaccording to one or more embodiments of the present technology.According to the embodiments shown in FIG. 2, image processing device200 can include memory 205, one or more processors 210, PET imagingcontroller 215, image acquisition module 220, motion characterizationmodule 225, gating module 230, amplitude characterization module 235,optimization module 240, and image processing module 245. Each of thesemodules can be embodied as special-purpose hardware (e.g., one or moreASICS, PLDs, FPGAs, or the like), or as programmable circuitry (e.g.,one or more microprocessors, microcontrollers, or the like)appropriately programmed with software and/or firmware, or as acombination of special purpose hardware and programmable circuitry.Other embodiments of the present technology may include some, all, ornone of these modules and components along with other modules,applications, and/or components. Still yet, some embodiments mayincorporate two or more of these modules and components into a singlemodule and/or associate a portion of the functionality of one or more ofthese modules with a different module. For example, in one embodiment,image processing device 200 may include a graphical user interface (GUI)generation module to generate one or more GUI screens that allow a userto review and/or select various settings or options.

Memory 205 can be any device, mechanism, or populated data structureused for storing information. In accordance with some embodiments of thepresent technology, memory 205 can encompass any type of, but is notlimited to, volatile memory, nonvolatile memory and dynamic memory. Forexample, memory 205 can be random access memory, memory storage devices,optical memory devices, media magnetic media, floppy disks, magnetictapes, hard drives, SDRAM, RDRAM, DDR RAM, erasable programmableread-only memories (EPROMs), electrically erasable programmableread-only memories (EEPROMs), compact disks, DVDs, and/or the like. Inaccordance with some embodiments, memory 205 may include one or moredisk drives, flash drives, one or more databases, one or more tables,one or more files, local cache memories, processor cache memories,relational databases, flat databases, and/or the like. In addition,those of ordinary skill in the art will appreciate many additionaldevices and techniques for storing information which can be used asmemory 205.

Memory 205 may be used to store instructions for running one or moreapplications or modules on processor(s) 210. For example, memory 205could be used in one or more embodiments to house all or some of theinstructions needed to execute the functionality of PET imagingcontroller 215, image acquisition module 220, motion characterizationmodule 225, gating module 230, amplitude characterization module 235,optimization module 240, and/or image processing module 240. Memory 205may also include an operating system that provides a software packagethat is capable of managing the hardware resources and provide commonservices for software applications running on processor(s) 210.

Imaging controller 215 can be configured to communicate with andgenerate commands to control an imaging system to scan a region ofinterest of a patient. The images can be stored in a database orprocessed in real-time (or near real-time) before being stored in thedatabase. Image acquisition module 220 can be configured to acquire theset of medical images of a patient collected via the medical imagingprocedure. The image acquisition can include, for example, the retrievalof the images from the database or management of the real-time (or nearreal-time) processing.

Motion characterization module 225 can be used to characterize patientmotion during scan acquisition. Motion characterization may be acquiredusing external hardware, or data driven strategies. Gating module 230can sort acquisition data relative to the phases derived from the motioncharacterization to generate phase gated data sets.

The amplitude characterization module 235 will derive a characterizationof the amplitude of patient motion as a function of the phases of motionusing data driven metrics. In some embodiments, amplitudecharacterization module 235 can apply a principal component analysis ofthe set of medical images to generate an indication amplitudefluctuations representing motion of the patient during the medicalimaging procedure. In addition, some embodiments of amplitudecharacterization module 235 can identify a center of mass measurement,calculate a gradient displacement, or a minimized a cost function,relative to the phases of the gated cycle.

Optimization module 240 can be configured to determine optimal binboundaries that maximize final image resolution. The characterization ofthe amplitude of patient motion generated with the amplitudecharacterization module 235 can be processed to determine the optimalbin boundaries. Image processing module 245 can then generate an optimalimage or a set of optimally gated images by resorting the image data 220relative to the optimal bin boundaries.

FIG. 3 is a flowchart illustrating a set of operations 300 forgenerating gated images according to one or more embodiments of thepresent technology. As illustrated in FIG. 3, during collectionoperation 310 a set of images (e.g., medical images) are collected. Thisset of images may be retrieved from a database on which the images arestored, collected directly from the imaging system, or from some othersource. Once the set of images have been collected, analysis operation320 is used to generate a principal component analysis which is astatistical procedure that uses an orthogonal transformation to converta set of observations of possibly correlated variables into a set ofvalues of linearly uncorrelated variables called principal components.Various implementation techniques can be used to efficiently generatedthe principal component analysis. The following document is incorporatedby reference herein for all purposes: “A tutorial on PrincipalComponents Analysis,” by Lindsay I. Smith, Feb. 26, 2002.

Using the results from the principal component analysis, motion of thepatient can be characterized from the images during characterizationoperation 330. Using this motion characterization, gating operation 340can be used to generated gated images based on the motioncharacterization. For example, in some embodiments, a weight factorarray of the first principal component can be used to define phasemotion amplitude relationship (see FIGS. 9 and 10). Using a weightedfactor array of second, third and/or fourth principal components todefine amplitude of non-first principal component fluctuations phasemotion amplitude relationship. The variations in the non-first principlecomponent fluctuations can be used to scale an “acceptance window”, thatwill define how close points on the first principle component phasemotion amplitude can be to be identified as “similar.” For example, anacceptance window may be defined as ±3 standard deviations non firstprinciple component fluctuations (see, e.g., FIG. 12).

Each point on the phase-motion amplitude curve (defined from firstprincipal components) can be analyzed to assess how many other curvedata points are within the amplitude acceptance window and can beclassified as similar. Whichever point has the most “similar” adjacentpoints then the phase corresponding to that point, and the other phasesclassified as “similar”, and the data corresponding to those phases canbe grouped together to form a single optimal data set. In accordancewith some embodiments, if multiple points are tied for most similaradjacent points, either can be used. In addition, it is possible for allpoints to be “similar”, indicating that the phase motion amplituderelationship is non-significant and 100% of data should be binnedtogether. Similarly, it is possible for no points to be similar,indicating that the phase motion amplitude relationship contained asignificant amount of motion, and that it is optimal not to groupsimilar data. Final images can then be created from optimal grouping ofdata sets.

FIG. 4 is a flowchart illustrating a set of operations 400 for analyzingimages according to one or more embodiments of the present technology.As illustrated in FIG. 4, analysis operation 410 analyzes the collectedimages characterize motion within a set of images. In accordance withvarious embodiments, this can be done by generating a data drivenamplitude-based motion characterization curve. This amplitude-basedcurve can be derived from amplitude characteristics with phase gateddata. Using this information, determination operation 420 can identifyan arrangement for optimally segregating data into gated images, or asingle optimal image. Using the information determination operation 430can determine if any gating is needed. When determination operation 430determines that no gating is needed then return operation 440 returnsthe non-gated images for use. When determination operation 430determines that gating will be beneficial (e.g., to improve imagequality), then gating operation 430 branches to generation operation 450where modified images are generated based on optimal window gating.

To implement gating, medical images must be acquired along with acharacterization of patient motion. This initial characterization isprovided by hardware or software (data driven gating) devices. Motioncharacterization can be used to gate data, using either phase-basedsubjugation of data (e.g. in the time domain), or by amplitude-basedmethods (e.g. in the motion amplitude domain). Often phase-based gatingis easier and more robust, but it has been shown that amplitude-basedgated images may be more useful clinically.

Because amplitude of motion and phase of motion correlate with the sameperiodicity, some embodiments are able to associate the two in a singleset of data. As a result, information in the phase-based subjugated datacan be used to derive an amplitude motion characterization.Specifically, some embodiments first generate an initialcharacterization of the gated data set representing the entire periodiccycle (cardiac, respiratory). This initial characterization isconstructed using traditional phase-based gating methodology. A newamplitude-based motion characterization can then be derived fromamplitude/correlative measures on the phase subjugated data, or throughcorrelation of subsets of raw data with phase subjugated data. The newamplitude-based motion characterization may then be used to subjugatedata into amplitude-based gating bins, or an optimal single bin, asdescribed in literature.

In some embodiments, the quality of the amplitude-based signal and/ormotion is evaluated, and used to determine optimal final segregation ofdata (1−n bins). For example, poorly defined amplitude motioncharacterization may indicate non-gated image data as optimal. In otherembodiments, the optimal final segregation of data can be derived usingstatistical modelling to determine the expected variations for the dataset's amplitude or correlative characterization. Data can then be sortedrelative to its amplitude (or correlative characterization) as well asits dependability from its separation from random fluctuations, derivedfrom the statistical modelling. Data can be sorted in either prereconstructed format and then reconstructed or post reconstructedformat.

In various embodiments, the optimal final segregation of data is derivedby comparing the amplitude curves from correctly and incorrectly sorteddata. Correctly sorted data (structured data set) is derived fromphase-based data segregation. Incorrectly sorted data (random data set)can be derived by random subjugation of data. Optimal final segregationof data will be derived relative to its amplitude (or correlative)characterization, as described by the structured data set, as well asits dependability derived from the comparison of structured and randomcurves. This process and example scenarios are described in more detailbelow.

FIG. 5 is a flowchart illustrating a set of operations 500 forgenerating an amplitude motion characterization curve andamplitude-based images through characterization of amplitude propertieson phase gated images in accordance with some embodiments of the presenttechnology. As illustrated in FIG. 5, acquisition operation 505 acquiresmotion characterization data along with the image acquisition operation510. Raw imaging data 515 can be segmented into a phase gated data setbased on the motion characterization 520 of the patient. The phase gateddata set 530 can be sorted into bins 530 and reconstructed into image535 which can then be used by derivation operation 540 to create a newmotion characterization derived from the gated images based measurements(e.g., center of mass measurement, gradient displacement, correlation,similarity, cost functions, etc.). Using the new motion characterizationalong with image data 515, amplitude gated image data set 545 can begenerated and separated into bins 550 and amplitude gated images 555.

In accordance with various embodiments, amplitude characterization canbe derived through representative amplitude measures: center of massdisplacement, optical flow, boundary motion, etc. Amplitudecharacterization can be derived, in some embodiments through correlativemeasures, e.g. correlations between short time data and phase gateddata, phase gated data and phase gated data, phase gated data and summeddata, phase gated data and subsets of phase gated data. Amplitudecharacterization of data can, in accordance with various embodiments,take place in both pre-reconstructed space and post reconstructed space.

FIG. 6 is a flowchart illustrating a set of operations 600 forgenerating an amplitude motion characterization curve andamplitude-based images through characterization of amplitude propertieson phase gated images in accordance with some embodiments of the presenttechnology. As illustrated in FIG. 6, acquisition operation 605 acquiresmotion characterization data along with the image acquisition operation610. Raw imaging data 615 can be segmented into a phase gated imagesbased on the motion characterization 620 of the patient. The phase gatedimage set 630 can be used by derivation operation 635 to create a newmotion characterization derived from the gated images based measurements(e.g., center of mass measurement, gradient displacement, correlation,similarity, cost functions, etc.). Using the new motion characterizationalong with image data 615, amplitude gated image data set 640 can begenerated and separated into bins 645 and amplitude gated images 650.

FIG. 7 is a flowchart illustrating a set of operations 700 forsegregation of data using structured data sets and random data sets inaccordance with some embodiments of the present technology. Asillustrated in FIG. 7, acquisition operation 710 can acquire images andprocessed to generate image data 715. Processing operation 720 cangenerate phase gated image data based on a structured set. Then,characterization operation 725 can generated an amplitude/correlationbased motion characterization. In addition, from image data 715, phasegated image data can be generated on an unstructured set duringprocessing operation 730. Then, using these phase gated images,characterization operation 735 can generate an amplitude/correlationbased motion characterization. Both of the motion characterization areused by sorting operation 740 to optimally sort the data based upon thecharacterizations from the structured data set and signal dependabilityfrom a comparison of the structured and unstructured (e.g., random) datasets. Using the optimal sorting, the image data can be created inimaging operation 745 along with amplitude images 750.

FIG. 8 illustrates a data driven motion curve from a patient withsignificant motion in accordance with various embodiments of the presenttechnology. This is an example of a derivation of amplitude-based motioncharacterizations derived using displacement or correlative measures.These characterizations are referred to as phase-amplitude transferfunctions. Once the data driven amplitude phase characterization curveis created, an optimal phase acceptance window can be selected asillustrated in FIG. 9.

Using the amplitude phase characterization curve and associated imagedata set, a determination can be made of which portions of the phasegated data set are ideal to combine to create a single optimal non-gatedimage instance. For example, the phase acceptance window may be selectedwindow where amplitude above or below threshold. The threshold may bedefined from curve (e.g. 35% of global amplitude shift) or by otherimage based metric (fluctuations in principle component weight vectors).The data can then be sorted and combined relative to window selection.Using the sorting based on the acceptance window, derived images can becreated. FIG. 10 illustrates images derived from signal binned, optimalbinned, and fully binned data in accordance with various embodiments ofthe present technology. In some embodiments, the image generated withoptimal segregation of data can makes use of gated information (i.e.improved resolution) while minimizing count statistics lost through datasegregation.

FIG. 11 illustrates a data driven amplitude phase characterization curvefrom a patient with non-significant motion in accordance with variousembodiments of the present technology. Once the data driven motion curvefrom the patient is created, an optimal phase acceptance window can beselected as illustrated in FIG. 12, with this case an example of a datasets with 100% of the statistics included in the optimal window. FIG. 13illustrates images derived from signal binned, optimal binned, and fullybinned data in accordance with various embodiments of the presenttechnology.

FIGS. 14A-14C illustrate various plots of data sorting determinationsbased upon phase amplitude characterization in accordance with variousembodiments of the present technology. Image data and patient motioncharacterization can be used in concert to generate a data drivenamplitude-based motion characterization. In accordance with variousembodiments, these techniques can result in a more robustcharacterization than those provided by external devices alone. Thesetechniques can be fully automated in various embodiments and allow foramplitude-based gating with data driven motion characterizationstrategies.

Optimal segregation of data can take place in both pre-reconstructedspace and post reconstructed space. Correlative/displacementmeasurements can take place in reconstructed or pre-reconstructed space.Short time data refers to any subset of total data Optimal finalsegregation of data will include 1-infiniti bins and may contain 0%-100%of raw data. Gated or optimized segregation of nuclear medicine or PETdata may be correlated with matched or unmatched transmission data forattenuation correction, as described in literature.

Exemplary Computer System Overview

Embodiments of the present invention include various steps andoperations, which have been described above. A variety of these stepsand operations may be performed by hardware components or may beembodied in machine-executable instructions, which may be used to causea general-purpose or special-purpose processor programmed with theinstructions to perform the steps. Alternatively, the steps may beperformed by a combination of hardware, software, and/or firmware. Assuch, FIG. 15 is an example of a computer system 1500 with whichembodiments of the present invention may be utilized. According to thepresent example, the computer system includes a bus 1510, at least oneprocessor 1520, at least one communication port 1530, a main memory1540, a removable storage media 1550, a read only memory 1560, and amass storage 1570.

Processor(s) 1520 can be any known processor, such as, but not limitedto, Intel® lines of processors; AMD® lines of processors; or Motorola®lines of processors. Communication port(s) 1530 can be any of an RS-232port for use with a modem-based dialup connection, a 10/100 Ethernetport, or a Gigabit port using copper or fiber. Communication port(s)1530 may be chosen depending on a network such as a Local Area Network(LAN), Wide Area Network (WAN), or any network to which the computersystem 1500 connects.

Main memory 1540 can be Random Access Memory (RAM) or any other dynamicstorage device(s) commonly known in the art. Read only memory 1560 canbe any static storage device(s) such as Programmable Read Only Memory(PROM) chips for storing static information such as instructions forprocessor 1520.

Mass storage 1570 can be used to store information and instructions. Forexample, hard disks such as the Adaptec® family of SCSI drives, anoptical disc, an array of disks such as RAID, such as the Adaptec familyof RAID drives, or any other mass storage devices may be used.

Bus 1510 communicatively couples processor(s) 1520 with the othermemory, storage and communication blocks. Bus 1510 can be a PCI/PCI-X orSCSI based system bus depending on the storage devices used.

Removable storage media 1550 can be any kind of external hard-drives,floppy drives, IOMEGA® Zip Drives, Compact Disc-Read Only Memory(CD-ROM), Compact Disc-Re-Writable (CD-RW), and/or Digital VideoDisk-Read Only Memory (DVD-ROM).

The components described above are meant to exemplify some types ofpossibilities. In no way should the aforementioned examples limit thescope of the invention, as they are only exemplary embodiments.

Embodiments of the present invention may be implemented using acombination of one or more modules. For example, embodiments provide fora graphical user interface generation module to generation one or moregraphical user interface screens to convey results/information and takeinstructions, a general-purpose or special-purpose “communicationsmodule” to receive and process various signals, as well as other modulesfor providing various functionality needed by embodiments of the presentinvention. Still yet, various embodiments may incorporate two or more ofthese modules into a single module and/or associate a portion of thefunctionality of one or more of these modules with a different module.

CONCLUSION

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, refer tothis application as a whole and not to any particular portions of thisapplication. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above Detailed Description of examples of the technology is notintended to be exhaustive or to limit the technology to the precise formdisclosed above. While specific examples for the technology aredescribed above for illustrative purposes, various equivalentmodifications are possible within the scope of the technology, as thoseskilled in the relevant art will recognize. For example, while processesor blocks are presented in a given order, alternative implementationsmay perform routines having steps, or employ systems having blocks, in adifferent order, and some processes or blocks may be deleted, moved,added, subdivided, combined, and/or modified to provide alternative orsubcombinations. Each of these processes or blocks may be implemented ina variety of different ways. Also, while processes or blocks are attimes shown as being performed in series, these processes or blocks mayinstead be performed or implemented in parallel, or may be performed atdifferent times. Further any specific numbers noted herein are onlyexamples: alternative implementations may employ differing values orranges.

The teachings of the technology provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther implementations of the technology. Some alternativeimplementations of the technology may include not only additionalelements to those implementations noted above, but also may includefewer elements.

These and other changes can be made to the technology in light of theabove Detailed Description. While the above description describescertain examples of the technology, and describes the best modecontemplated, no matter how detailed the above appears in text, thetechnology can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the technology disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the technology should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the technology with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the technology to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe technology encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the technology under theclaims.

To reduce the number of claims, certain aspects of the technology arepresented below in certain claim forms, but the applicant contemplatesthe various aspects of the technology in any number of claim forms. Forexample, while only one aspect of the technology is recited as acomputer-readable medium claim, other aspects may likewise be embodiedas a computer-readable medium claim, or in other forms, such as beingembodied in a means-plus-function claim. Any claims intended to betreated under 35 U.S.C. § 112(f) will begin with the words “means for”,but use of the term “for” in any other context is not intended to invoketreatment under 35 U.S.C. § 112(f). Accordingly, the applicant reservesthe right to pursue additional claims after filing this application topursue such additional claim forms, in either this application or in acontinuing application.

What is claimed is:
 1. A method comprising: acquiring a set of phasegated medical images of a patient collected via a medical imagingprocedure; applying a principal component analysis to generate a set ofprincipal components that include a first principal component and anon-first principal component; wherein the principal component analysisis applied across phases of the set of phase gated medical images togenerate a phase-data driven motion amplitude curve describing anamplitude of motion of the patient during the medical imaging procedureas a function of phase of a periodic motion cycle, wherein thephase-data driven motion amplitude curve is based on a weight factorarray of the first principal component; identifying an acceptance windowbased on variations in weight factor of fluctuations in the non-firstprincipal component; determining an optimal segregation of image databased on the phase-data driven motion amplitude curve relative to theacceptance window; and generating one or more medical images based onthe optimal segregation of the image data.
 2. The method of claim 1,wherein determining the optimal segregation of the image data includesanalyzing each point on the phase-motion amplitude curve to assess howmany other curve data points are included within the acceptance window.3. The method of claim 2, wherein determining the optimal segregationincludes: classifying each image based on placement on the phase-motionamplitude curve; and grouping medical images from the set of phase gatedmedical images based on the classification.
 4. The method of claim 1,wherein multiple principal components are generated in the principalcomponent analysis.
 5. The method of claim 1, further comprisingcharacterizing a position of the patient within each medical image inthe set of phase gated medical images by using one or more of acorrelative measures or signal displacement measures.
 6. The method inclaim 1, wherein the set of phase gated medical images may be either prereconstructed or post reconstructed embodiments of image data.
 7. Themethod of claim 1, wherein the principal component analysis of the setof phase gated medical images includes identifying a set of pixelsacross each medical image in the set of phase gated medical imagesrepresenting a common point of interest.
 8. The method of claim 1,wherein the medical imaging procedure includes positron emissiontomography, magnetic resonance imaging, ultrasound, single-photonemission computed tomography, or planar gamma camera imaging.
 9. Amethod comprising: acquiring a set of phase gated medical images of apatient collected via a medical imaging procedure; applying data drivenmeasures to characterize amplitude of patient motion relative to phase;segregating data based on the data driven amplitude of motioncharacterization relative to phase to achieve a desired balance betweennoise and resolution; and generating a set of one or more motioncorrected medical images based on segregation of the data.
 10. Themethod of claim 9, wherein characterizing the motion of the patient isbased on a center of mass measurement, gradient displacement, a costfunction measured on the gated images, or a correlative measures betweengated images.
 11. The method of claim 9, wherein characterizing themotion of the patient is based on principal component analysis of theset of phase gated images.
 12. The method of claim 9, wherein tocharacterize the amplitude of the patient motion in the set of phasegated medical images is limited to a subset of pixels across eachmedical image in the set of phase gated medical images representing acommon volume of interest.
 13. The method of claim 9, furthercomprising: analyzing the indication of phase and amplitude fluctuationsto determine optimal bin sizes or equal bin sizes for sorting the set ofmedical images to maximize image resolution; and generating a set ofgated medical images based on sorting of image data.
 14. The method ofclaim 13, wherein the set of gated medical images are only generatedwhen the image resolution is improved by at least a set threshold. 15.The method of claim 9, wherein the medical imaging procedure includespositron emission tomography, magnetic resonance imaging, ultrasound,single-photon emission computed tomography, or planar gamma cameraimaging.
 16. A system comprising: a memory; a processor; an imageacquisition module, under the control of the processor, configured toacquire a set of medical images of a patient collected via a medicalimaging procedure; a motion characterization module, under the controlof the processor, configured to characterize gating motion of thepatient based on the set of medical images; an optimization module,under the control of the processor, configured to determine optimal binsizes that maximize image resolution; and an image processing module,under the control of the processor, configured to generate a set ofgated medical images by sorting the set of medical images into binsbased on the optimal bin sizes and combining medical images within eachof the bins to create the set of gated medical images.
 17. The system ofclaim 16, wherein the motion characterization module applies a principalcomponent analysis of the set of medical images to generate anindication of phase and amplitude fluctuations representing gating ofthe patient during the medical imaging procedure.
 18. The system ofclaim 16, wherein the motion characterization module is furtherconfigured to identify a center of mass measurement, calculate agradient displacement, or a minimize a cost function.
 19. The system ofclaim 16, wherein the motion characterization module is furtherconfigured to: generate an amplitude phase characterization curve; andcharacterize a position of the patient within each medical image in theset of medical images by using one or more of a correlative measure, asignal displacement measure, or a principal component analysis.
 20. Thesystem of claim 19, wherein the correlative measure is a Pearsoncorrelation or a mutual information correlation and the signaldisplacement measure is based on a center of mass.